blue-yonder/tsfresh

Features

• Statistical Feature Extractors - Calculates comprehensive statistical characteristics from raw time series sequences to create tabular datasets for machine learning.
• Automated Feature Selection Tools - Automatically identifies and retains only the most predictive characteristics to reduce dimensionality and prevent overfitting.
• Forecasting Input Preparation - Formats temporal data into rolling window sets for training and evaluating forecasting models.
• Irrelevant Feature Filters - Removes characteristics that do not contribute to the target task by evaluating explaining power via hypothesis testing.
• Tabular Converters - Transforms raw time series sequences into flat tabular dataframes suitable for standard machine learning models.
• Hypothesis Testing Frameworks - Implements rigorous statistical tests, such as p-value calculations, to validate the significance of extracted features.
• Time Series Forecasting - Prepares temporal data sequences for prediction models by transforming them into rolling window frames.
• Time Series Machine Learning - Preprocesses temporal and sequential data through imputation and rolling window generation for machine learning.
• Feature Engineering Tools - Provides an automated system for transforming raw sequence data into predictive features through statistical calculations.
• Parallel Processing - Distributes heavy statistical feature extraction workloads across multiple CPU cores to process large time series datasets efficiently.
• Time-Series Statistical Profiling - Extracts complex statistical characteristics from time-indexed data using optimized vectorized operations.
• Time Series Toolkits - Offers a comprehensive toolkit for automatically extracting statistical characteristics from raw time series data.
• Forecasting Window Generators - Generates overlapping rolling window frames from sequential data to prepare training sets for time-dependent forecasting.
• Large-Scale Data Computation - Processes massive time series datasets by distributing heavy computational workloads across multiple CPU cores.
• Custom Parallel Task Execution - Distributes heavy feature calculation workloads across multiple CPU cores using a worker-based task queue.
• Machine Learning Pipelines - Integrates data extraction and filtering steps into a unified workflow for simplified model training and deployment.
• Compositional Transformation Pipelines - Chains feature extraction, imputation, and selection into a single reproducible sequence for preprocessing.
• Feature Engineering - Automatic extraction of features from time series data.
• Time Series Analysis - Automatic feature extraction for time series datasets.
• Feature Engineering - Automatic feature extraction from time series data.
• Time Series Analysis - Automated feature engineering for time series data.
• Automated Machine Learning - Automatic extraction of relevant features from time series data.

Star history